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1 | Description | |||||||||||||||||
2 | Metadata | Metadata not available in standardised, machine-readable format I | ||||||||||||||||
3 | Interoperability of different metadata standards (e.g. ISO 19139, GeoSource, SDI) | |||||||||||||||||
4 | Release schedule of data not clear from metadata | |||||||||||||||||
5 | Who is supporting, and how it is supported, the data not clear from metadata | |||||||||||||||||
6 | Granularity | Unify way to access geodata at different level e.g.:, local, regional, national or EU level. | ||||||||||||||||
7 | Deciding granularity of data to publish | |||||||||||||||||
8 | Formats | Data provided in just one format (csv) | ||||||||||||||||
9 | Transformation of data into Open Data formats | |||||||||||||||||
10 | Different Indicators, Different Temporal and Spatial Granularity | |||||||||||||||||
11 | Heterogeneous Formats (CSV != CSV) ... Maybe the W3C CSV on the Web WG will solve this issue) | |||||||||||||||||
12 | Vocabularies | Common vocabularies are not used, which is a barrier for data integration II | ||||||||||||||||
13 | Added value comes from comparable Open datasets being combined | |||||||||||||||||
14 | Use of linked open vocabularies and domain vocabularies to make the study data searchable. | |||||||||||||||||
15 | What vocabularies should be reused | |||||||||||||||||
16 | Dataset versioning | |||||||||||||||||
17 | Dataset selection | Identifying what data to publish online | ||||||||||||||||
18 | Processing | Extraction of original data | ||||||||||||||||
19 | There is no feedback loop to incorporate data corrections back into the original data (automated/machine-readable?) | |||||||||||||||||
20 | Difficult to track data usage | |||||||||||||||||
21 | Static/Real-time | Data not available in real-time | ||||||||||||||||
22 | Data not available in bulk | |||||||||||||||||
23 | Open Data quickly becomes stale - automate the data publishing process to keep data up to date and accurate | |||||||||||||||||
24 | Tools | Selection, configuration and installation of OD catalogue tool | ||||||||||||||||
25 | Privacy/security | High-value datasets (critical infrastructure, utility services, road networks,) are not released as they are deemed to pose a risk to security | ||||||||||||||||
26 | Privacy: some data is personal or may become so when linked to others | |||||||||||||||||
27 | Quality | Incomplete data | ||||||||||||||||
28 | Incomparable data | |||||||||||||||||
29 | How to measure the quality of the data | |||||||||||||||||
30 | How to provide information about the quality of the data | |||||||||||||||||
31 | Ensuring the quality of data before it is released | |||||||||||||||||
32 | Licenses | Licenses not standardised II | ||||||||||||||||
33 | Licenses combination. What is the resulting license of combined dataset? | |||||||||||||||||
34 | Licenses not machine-readable | |||||||||||||||||
35 | Licenses not interoperable | |||||||||||||||||
36 | Industry-reuse | Industrial uptake of Open Data difficult – different requirements I | ||||||||||||||||
37 | SLAs not standardised | |||||||||||||||||
38 | SLAs not machine-readable | |||||||||||||||||
39 | APIs | APIs can be too clunky/rich in their functionality, which may increase the amount of calls necessary and size of data transferred, reducing performance | ||||||||||||||||
40 | Collaboration between API providers and users is necessary to agree on 'useful' calls | |||||||||||||||||
41 | API key agreements could restrict Openess of Open Data? | |||||||||||||||||
42 | Documentation accompanying APIs can be lacking | |||||||||||||||||
43 | What is best practice for publishing streams of real-time data (with/without APIs)? | |||||||||||||||||
44 | For accessing numerous datasets scientists will be accessing the archive directly using other protocols such as sftp, rsync, scp, access techniques such as: http://www.psc.edu/index.php/hpn-ssh | |||||||||||||||||
45 | For accessing individual datasets a REST GET interface to the archive should be provided. | |||||||||||||||||
46 | URIs | Each resource should have one URI uniquly identifying it. There can then be different representations of the resource (xml/html/json/rdf) | ||||||||||||||||
47 | Skills/Expertise | Expertise: Data owners do not have the skills to publish data | ||||||||||||||||
48 | Capacity: Data owners do not have the resources to curate and publish data | |||||||||||||||||
49 | Education about Open Data not provided | |||||||||||||||||
50 | Cultural: Data owners are acustomed to a particular approach in their community | |||||||||||||||||
51 | Revenue | Revenue: institutions working under mixed gov/non-gov funding generate part of their revenue by selling some of the data they curate. Switching to an open data model will generate a direct loss in revenue that has to be backed-up by other means. This does not have to mean closing the data, e.g. a model of open dereferencing + paid dumps can be considered, as well as other indirect revenue streams. | ||||||||||||||||
52 | Archiving | Preservation (digital archiving) of Linked Data (taking the LD off-line as a dump and putting in an archive effectively turns in into dead dataset) | ||||||||||||||||
53 | Decide on the importance of the de-referencability of resources and the potential implications for domain names and naming of resources. Decide on the scope of the step that will turn a connected sub-graph into an isolated data dump. | |||||||||||||||||
54 | Policy | National policy: data must be made open by default | ||||||||||||||||
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